用于放射学报告多语言结构化表示的知识图谱嵌入
Knowledge Graph Embeddings for Multi-Lingual Structured Representations of Radiology Reports
AI总结：
提出一种轻量级基于图的放射学报告嵌入方法，通过多语言SNOMED知识库连接医学术语，在疾病分类和图像分类任务中表现优异且模型规模与数据需求更小。
摘要：
近年来，我们分析临床文本的方式发生了重大变化。BERT等语言模型的引入催生了针对（生物）医学领域的改编版本，如PubMedBERT和ClinicalBERT。这些模型依赖于庞大的归档医疗文档数据库。尽管在准确率方面表现良好，但缺乏可解释性以及跨语言迁移的局限性，限制了它们在临床环境中的使用。我们引入了一种新颖的轻量级基于图的嵌入方法，专门针对放射学报告。该方法考虑了报告的结构和组成，同时通过多语言SNOMED Clinical Terms知识库连接报告中的医学术语。生成的图嵌入揭示了临床术语之间的潜在关系，在不依赖大型预训练数据集的情况下，实现了一种临床医生更容易理解且临床准确性更高的表示。我们在两个任务上展示了该嵌入的使用，即X射线报告的疾病分类和图像分类。对于疾病分类，我们的模型与基于BERT的同类模型具有竞争力，同时其模型规模和训练数据需求要小几个数量级。对于图像分类，我们展示了利用跨模态知识转移的图嵌入的有效性，并展示了该方法如何适用于不同语言。
英文摘要：
The way we analyse clinical texts has undergone major changes over the last years. The introduction of language models such as BERT led to adaptations for the (bio)medical domain like PubMedBERT and ClinicalBERT. These models rely on large databases of archived medical documents. While performing well in terms of accuracy, both the lack of interpretability and limitations to transfer across languages limit their use in clinical setting. We introduce a novel light-weight graph-based embedding method specifically catering radiology reports. It takes into account the structure and composition of the report, while also connecting medical terms in the report through the multi-lingual SNOMED Clinical Terms knowledge base. The resulting graph embedding uncovers the underlying relationships among clinical terms, achieving a representation that is better understandable for clinicians and clinically more accurate, without reliance on large pre-training datasets. We show the use of this embedding on two tasks namely disease classification of X-ray reports and image classification. For disease classification our model is competitive with its BERT-based counterparts, while being magnitudes smaller in size and training data requirements. For image classification, we show the effectiveness of the graph embedding leveraging cross-modal knowledge transfer and show how this method is usable across different languages.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
